Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder with complex genetics. ASD is highly heritable and affects around 1% of the population. To better understand ASD and develop clinical treatments, it is essential to understand the genetic component of its etiology. However, it has become clear that hundreds of genes are likely involved in ASD pathogenesis, so the need to evaluate the human genome in a deep, unbiased manner is greater than ever. Effectively interpreting and testing hypotheses from genome- wide information is made possible by bioinformatic tools such as co-expression network analysis, which identifies groups of genes, known as modules, by their shared patterns of RNA transcript expression across biological conditions. In this project, my goal is to significantly extend our current knowledge of transcriptional regulation and dysregulation across human brain regions through the integration of transcription factors (TFs), microRNAs (miRNAs), and their effect on the transcription of target genes using co-expression networks and regulatory molecule binding site information. To accomplish this, I will use data from RNA sequencing in post- mortem brain, which enables accurate quantification of protein coding RNA and noncoding RNA levels. I will apply co-expression network analysis to summarize tens of thousands of genetic changes into a manageable number of gene groups or modules, which I will characterize for expression patterns related to brain region, cell-type, and intracellular organelle. In Aim 1, I will define modules in a transcriptional network from sixteen brain regions across normal aging to characterize the transcriptional architecture of normal brain development and function. I will then apply TF and miRNA binding site enrichment analysis in modules from this network to predict, characterize, and prioritize transcriptional regulators affecting genes essential to normal brain development and function. In Aim 2, I will apply co-expression network analysis to RNA-seq data from five ASD-related brain regions in post-mortem brains from ASD and control individuals in a different sample set. I will characterize modules as in Aim 1, but also by expression pattern association to ASD. I will then apply regulatory molecule binding site enrichment in ASD-associated modules to identify TFs and miRNAs involved in dysregulated pathways. I will assess whether these dysregulated pathways map to pathways important in prenatal and early brain development. I will also assess if dysregulated pathways are enriched for common and rare genetic variants implicated in genomic studies of ASD. In Aim 3, as a key proof of principle, I will validate candidate regulators and their effect on target pathways by knockdown of prioritized TFs and miRNAs during the differentiation of normal human neural progenitor cells in vitro. This project integrates neuroscience, genetics, and bioinformatics to systematically identify and validate novel ASD-associated disruption in transcriptional regulation of coding and noncoding RNA in brain. Validated TFs and miRNAs are likely to be essential transcriptional regulators for brain development and may be potential therapeutic targets in ASD.